diff --git a/docs/modules/ROOT/pages/saliency-explanations-on-odh.adoc b/docs/modules/ROOT/pages/saliency-explanations-on-odh.adoc
index 3342533..baa06d9 100644
--- a/docs/modules/ROOT/pages/saliency-explanations-on-odh.adoc
+++ b/docs/modules/ROOT/pages/saliency-explanations-on-odh.adoc
@@ -1,6 +1,6 @@
 = Saliency explanations on ODH
 
-This tutorial will walk you through setting up and using TrustyAI to provide saliency explanations for model predictions within a OpenShift environment using OpenDataHub. We will deploy a model, configure the environment, and demonstrate how to obtain predictions and their explanations.
+This tutorial will walk you through setting up and using TrustyAI to provide saliency explanations for model inferences within a OpenShift environment using OpenDataHub. We will deploy a model, configure the environment, and demonstrate how to obtain inferences and their explanations.
 
 [NOTE]
 ====
@@ -107,12 +107,12 @@ export TOKEN=$(oc whoami -t)
 
 == Requesting Explanations
 
-=== Issue a Prediction
+=== Request an inference
 
-In order to obtain an explanation, we first need to make a prediction.
-The explanation request will be based on this prediction ID.
+In order to obtain an explanation, we first need to make an inference.
+The explanation request will be based on this inference ID.
 
-Start by sending an inference request to the model to get a prediction. Replace `${TOKEN}` with your actual authorization token.
+Start by sending an inference request to the model to get an inference. Replace `${TOKEN}` with your actual authorization token.
 
 [source,shell]
 ----
@@ -121,26 +121,46 @@ curl -skv -H "Authorization: Bearer ${TOKEN}" \
    -d '{"inputs": [{"name": "predict","shape": [1,5], "datatype": "FP64", "data": [1.0, 2.0, 1.0, 0.0, 1.0]}]}'
 ----
 
-=== Get a Random Prediction ID
+=== Getting an inference ID
 
-Extract the latest prediction ID for use in obtaining an explanation.
+The TrustyAI service provides an endpoint to list stored inference ids.
+You can list all (non-synthetic or _organic_) ids by running:
 
 ```shell
-export PREDICTION_ID=$(oc exec $TRUSTYAI_POD -n explainer-tests -c trustyai-service -- sh -c "awk -F',' '{print \$2}' /inputs/explainer-test-internal_data.csv | tail -n 1")
+curl -skv -H "Authorization: Bearer ${TOKEN}" \
+   https://${TRUSTYAI_ROUTE}/info/inference/ids/explainer-test?type=organic
+```
+
+The response will be similar to
+
+```json
+[
+  {
+    "id":"a3d3d4a2-93f6-4a23-aedb-051416ecf84f",
+    "timestamp":"2024-06-25T09:06:28.75701201"
+  }
+]
+```
+
+Extract the latest inference ID for use in obtaining an explanation.
+
+```shell
+export INFERENCE_ID=$(curl -skv -H "Authorization: Bearer ${TOKEN}" \
+  https://${TRUSTYAI_ROUTE}/info/inference/ids/explainer-test?type=organic | jq -r '.[-1].id')
 ```
 
 === Request a LIME Explanation
 
 We will use LIME as our explainer for this tutorial. More information on LIME can be found xref:local-explainers.adoc#LIME[here].
 
-Request a LIME explanation for the selected prediction ID.
+Request a LIME explanation for the selected inference ID.
 
 [source,shell]
 ----
 curl -sk -X POST -H "Authorization: Bearer ${TOKEN}" \
     -H "Content-Type: application/json" \
     -d "{
-    \"predictionId\": \"$PREDICTION_ID\",
+    \"predictionId\": \"$INFERENCE_ID\",
     \"modelConfig\": {
         \"target\": \"modelmesh-serving.${NAMESPACE}.svc.cluster.local:8033\",
         \"name\": \"explainer-test\",
@@ -153,7 +173,7 @@ curl -sk -X POST -H "Authorization: Bearer ${TOKEN}" \
 
 === Results
 
-The output will show the saliency scores and confidence for each input feature used in the prediction.
+The output will show the saliency scores and confidence for each input feature used in the inference.
 
 [source,json]
 ----